VMware vSphere and Librato Integration

Input and output integration overview

The VMware vSphere Telegraf plugin provides a means to collect metrics from VMware vCenter servers, allowing for comprehensive monitoring and management of virtual resources in a vSphere environment.

The Librato plugin for Telegraf is designed to facilitate seamless integration with the Librato Metrics API, allowing for efficient metric reporting and monitoring.

Integration details

VMware vSphere

This plugin connects to VMware vSphere servers to gather a variety of metrics from virtual environments, enabling efficient monitoring and management of virtual resources. It interfaces with the vSphere API to collect statistics regarding clusters, hosts, resource pools, VMs, datastores, and vSAN entities, presenting them in a format suitable for analysis and visualization. The plugin is particularly valuable for administrators who manage VMware-based infrastructures, as it helps to track system performance, resource usage, and operational issues in real-time. By aggregating data from multiple sources, the plugin empowers users with insights that facilitate informed decision-making regarding resource allocation, troubleshooting, and ensuring optimal system performance. Additionally, the support for secret-store integration allows secure handling of sensitive credentials, promoting best practices in security and compliance assessments.

Librato

The Librato plugin enables Telegraf to send metrics to the Librato Metrics API. To authenticate, users must provide an api_user and api_token, which can be acquired from the Librato account settings. This integration allows for efficient monitoring and reporting of custom metrics within the Librato platform. The plugin also utilizes a source_tag option that can enrich the metrics with contextual information from Point Tags; however, it does not currently support sending associated Point Tags. It is essential to note that any point value sent that cannot be converted to a float64 type will be skipped, ensuring that only valid metrics are processed and sent to Librato. The plugin also supports secret-store options for managing sensitive authentication credentials securely, facilitating best practices in credential management.

Configuration

VMware vSphere

[[inputs.vsphere]]
  vcenters = [ "https://vcenter.local/sdk" ]
  username = "[email protected]"
  password = "secret"

  vm_metric_include = [
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.run.summation",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.wait.summation",
    "mem.active.average",
    "mem.granted.average",
    "mem.latency.average",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.usage.average",
    "power.power.average",
    "virtualDisk.numberReadAveraged.average",
    "virtualDisk.numberWriteAveraged.average",
    "virtualDisk.read.average",
    "virtualDisk.readOIO.latest",
    "virtualDisk.throughput.usage.average",
    "virtualDisk.totalReadLatency.average",
    "virtualDisk.totalWriteLatency.average",
    "virtualDisk.write.average",
    "virtualDisk.writeOIO.latest",
    "sys.uptime.latest",
  ]

  host_metric_include = [
    "cpu.coreUtilization.average",
    "cpu.costop.summation",
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.swapwait.summation",
    "cpu.usage.average",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.utilization.average",
    "cpu.wait.summation",
    "disk.deviceReadLatency.average",
    "disk.deviceWriteLatency.average",
    "disk.kernelReadLatency.average",
    "disk.kernelWriteLatency.average",
    "disk.numberReadAveraged.average",
    "disk.numberWriteAveraged.average",
    "disk.read.average",
    "disk.totalReadLatency.average",
    "disk.totalWriteLatency.average",
    "disk.write.average",
    "mem.active.average",
    "mem.latency.average",
    "mem.state.latest",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.totalCapacity.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.errorsRx.summation",
    "net.errorsTx.summation",
    "net.usage.average",
    "power.power.average",
    "storageAdapter.numberReadAveraged.average",
    "storageAdapter.numberWriteAveraged.average",
    "storageAdapter.read.average",
    "storageAdapter.write.average",
    "sys.uptime.latest",
  ]

  datacenter_metric_include = [] ## if omitted or empty, all metrics are collected
  datacenter_metric_exclude = [ "*" ] ## Datacenters are not collected by default.

  vsan_metric_include = [] ## if omitted or empty, all metrics are collected
  vsan_metric_exclude = [ "*" ] ## vSAN are not collected by default.

  separator = "_"
  max_query_objects = 256
  max_query_metrics = 256
  collect_concurrency = 1
  discover_concurrency = 1
  object_discovery_interval = "300s"
  timeout = "60s"
  use_int_samples = true
  custom_attribute_include = []
  custom_attribute_exclude = ["*"]
  metric_lookback = 3
  ssl_ca = "/path/to/cafile"
  ssl_cert = "/path/to/certfile"
  ssl_key = "/path/to/keyfile"
  insecure_skip_verify = false
  historical_interval = "5m"
  disconnected_servers_behavior = "error"
  use_system_proxy = true
  http_proxy_url = ""

Librato

[[outputs.librato]]
  ## Librato API Docs
  ## http://dev.librato.com/v1/metrics-authentication
  ## Librato API user
  api_user = "[email protected]" # required.
  ## Librato API token
  api_token = "my-secret-token" # required.
  ## Debug
  # debug = false
  ## Connection timeout.
  # timeout = "5s"
  ## Output source Template (same as graphite buckets)
  ## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md#graphite
  ## This template is used in librato's source (not metric's name)
  template = "host"

Input and output integration examples

VMware vSphere

1. Dynamic Resource Allocation: Utilize this plugin to monitor resource usage across a fleet of VMs and automatically adjust resource allocations based on performance metrics. This scenario could involve triggering scaling actions in real time based on CPU and memory usage metrics collected from the vSphere API, ensuring optimal performance and cost-efficiency.

2. Capacity Planning and Forecasting: Leverage the historical metrics gathered from vSphere to conduct capacity planning. Analyzing the trends of CPU, memory, and storage usage over time helps administrators anticipate when additional resources will be needed, avoiding outages and ensuring that the virtual infrastructure can handle growth.

3. Automated Alerting and Incident Response: Integrate this plugin with alerting tools to set up automated notifications based on the metrics gathered. For example, if the CPU usage on a host exceeds a specified threshold, it could trigger alerts and automatically initiate predefined remediation steps, such as migrating VMs to less utilized hosts.

4. Performance Benchmarking Across Clusters: Use the metrics collected to compare the performance of clusters in different vCenters. This benchmarking provides insights into which cluster configurations yield the best resource efficiency and can guide future infrastructure enhancements.

Librato

1. Real-time Application Monitoring: Utilize Librato to collect performance metrics from a web application in real-time. This setup involves sending response times, error rates, and user interactions to Librato, allowing developers to monitor the application’s health and performance metrics closely. By analyzing these metrics, teams can quickly identify and address performance bottlenecks or application failures before they impact end users.

2. Infrastructure Metrics Aggregation: Leverage this plugin to gather and send metrics from various infrastructure components, such as servers or containers, to Librato for centralized monitoring. Configuring the plugin to send CPU, memory usage, and disk I/O metrics enables system administrators to have a comprehensive view of infrastructure performance, assisting in capacity planning and resource optimization strategies.

3. Custom Metrics for Business Operations: Feed business-specific metrics, such as sales transactions or user sign-ups, to the Librato service using this plugin. By tracking these custom metrics, businesses can gain insights into their operational performance and make data-driven decisions to enhance their strategies, marketing efforts, or product development initiatives.

4. Anomaly Detection in Metrics: Implement monitoring tools that utilize machine learning for anomaly detection. By continuously sending real-time metrics to Librato, teams can analyze trends and automatically flag unusual behavior, such as sudden spikes in latency or unusual traffic patterns, enabling timely intervention and troubleshooting.

Feedback

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